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arXiv · 2410.07282

A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences

Danny Y. C. Wang·Lars Arne Jordanger·Jerry Chun-Wei Lin
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Abstract

In rapidly evolving e-commerce industry, the capability of selecting high-quality data for model training is essential. This study introduces the High-Utility Sequential Pattern Mining using SHAP values (HUSPM-SHAP) model, a utility mining-based active learning strategy to tackle this challenge. We found that the parameter settings for positive and negative SHAP values impact the model's mining outcomes, introducing a key consideration into the active learning framework. Through extensive experiments aimed at predicting behaviors that do lead to purchases or not, the designed HUSPM-SHAP model demonstrates its superiority across diverse scenarios. The model's ability to mitigate labeling needs while maintaining high predictive performance is highlighted. Our findings demonstrate the model's capability to refine e-commerce data processing, steering towards more streamlined, cost-effective prediction modeling.

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Danny Y. C. Wang, Lars Arne Jordanger, Jerry Chun-Wei Lin. 2024-10-09. A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences. https://arxiv.org/abs/2410.07282

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